Overview

Dataset statistics

Number of variables12
Number of observations273
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory28.7 KiB
Average record size in memory107.5 B

Variable types

Numeric8
Text1
Categorical3

Dataset

Description파일 다운로드
Author서울교통공사
URLhttps://data.seoul.go.kr/dataList/OA-13294/F/1/datasetView.do

Alerts

연번 is highly overall correlated with 호선 and 4 other fieldsHigh correlation
호선 is highly overall correlated with 연번 and 4 other fieldsHigh correlation
역번호 is highly overall correlated with 연번 and 4 other fieldsHigh correlation
턴스타일개집표기 is highly overall correlated with 연번 and 4 other fieldsHigh correlation
플랩형개집표기 is highly overall correlated with 연번 and 4 other fieldsHigh correlation
스피드개집표기 is highly overall correlated with 연번 and 4 other fieldsHigh correlation
개방형개집표기 is highly imbalanced (95.6%)Imbalance
엘레베이터용개집표기 is highly imbalanced (58.9%)Imbalance
연번 has unique valuesUnique
역번호 has unique valuesUnique
턴스타일개집표기 has 168 (61.5%) zerosZeros
슬림개집표기 has 241 (88.3%) zerosZeros
플랩형개집표기 has 122 (44.7%) zerosZeros
표준형개집표기 has 261 (95.6%) zerosZeros
스피드개집표기 has 157 (57.5%) zerosZeros

Reproduction

Analysis started2024-04-29 16:50:06.752769
Analysis finished2024-04-29 16:50:12.867241
Duration6.11 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct273
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean137
Minimum1
Maximum273
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-04-30T01:50:12.933237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14.6
Q169
median137
Q3205
95-th percentile259.4
Maximum273
Range272
Interquartile range (IQR)136

Descriptive statistics

Standard deviation78.952517
Coefficient of variation (CV)0.57629575
Kurtosis-1.2
Mean137
Median Absolute Deviation (MAD)68
Skewness0
Sum37401
Variance6233.5
MonotonicityStrictly increasing
2024-04-30T01:50:13.072294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.4%
181 1
 
0.4%
187 1
 
0.4%
186 1
 
0.4%
185 1
 
0.4%
184 1
 
0.4%
183 1
 
0.4%
182 1
 
0.4%
180 1
 
0.4%
206 1
 
0.4%
Other values (263) 263
96.3%
ValueCountFrequency (%)
1 1
0.4%
2 1
0.4%
3 1
0.4%
4 1
0.4%
5 1
0.4%
6 1
0.4%
7 1
0.4%
8 1
0.4%
9 1
0.4%
10 1
0.4%
ValueCountFrequency (%)
273 1
0.4%
272 1
0.4%
271 1
0.4%
270 1
0.4%
269 1
0.4%
268 1
0.4%
267 1
0.4%
266 1
0.4%
265 1
0.4%
264 1
0.4%

호선
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6043956
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-04-30T01:50:13.196364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.6
Q13
median5
Q36
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.0339713
Coefficient of variation (CV)0.44174557
Kurtosis-1.1324876
Mean4.6043956
Median Absolute Deviation (MAD)2
Skewness-0.091375877
Sum1257
Variance4.1370394
MonotonicityIncreasing
2024-04-30T01:50:13.289113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
5 55
20.1%
2 46
16.8%
7 43
15.8%
6 38
13.9%
3 33
12.1%
4 26
9.5%
8 18
 
6.6%
1 14
 
5.1%
ValueCountFrequency (%)
1 14
 
5.1%
2 46
16.8%
3 33
12.1%
4 26
9.5%
5 55
20.1%
6 38
13.9%
7 43
15.8%
8 18
 
6.6%
ValueCountFrequency (%)
8 18
 
6.6%
7 43
15.8%
6 38
13.9%
5 55
20.1%
4 26
9.5%
3 33
12.1%
2 46
16.8%
1 14
 
5.1%

역번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct273
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1619.326
Minimum150
Maximum2828
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-04-30T01:50:13.405965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum150
5-th percentile204.6
Q1317
median2528
Q32640
95-th percentile2814.4
Maximum2828
Range2678
Interquartile range (IQR)2323

Descriptive statistics

Standard deviation1174.3885
Coefficient of variation (CV)0.72523289
Kurtosis-1.922557
Mean1619.326
Median Absolute Deviation (MAD)283
Skewness-0.25469603
Sum442076
Variance1379188.3
MonotonicityNot monotonic
2024-04-30T01:50:13.547750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 1
 
0.4%
2617 1
 
0.4%
2623 1
 
0.4%
2622 1
 
0.4%
2621 1
 
0.4%
2620 1
 
0.4%
2619 1
 
0.4%
2618 1
 
0.4%
2616 1
 
0.4%
2642 1
 
0.4%
Other values (263) 263
96.3%
ValueCountFrequency (%)
150 1
0.4%
151 1
0.4%
152 1
0.4%
153 1
0.4%
154 1
0.4%
155 1
0.4%
156 1
0.4%
157 1
0.4%
158 1
0.4%
159 1
0.4%
ValueCountFrequency (%)
2828 1
0.4%
2827 1
0.4%
2826 1
0.4%
2825 1
0.4%
2824 1
0.4%
2823 1
0.4%
2822 1
0.4%
2821 1
0.4%
2820 1
0.4%
2819 1
0.4%
Distinct254
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2024-04-30T01:50:13.832624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length3.2051282
Min length2

Characters and Unicode

Total characters875
Distinct characters217
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique236 ?
Unique (%)86.4%

Sample

1st row서울역(1)
2nd row시청(1)
3rd row종각
4th row종로3가(1)
5th row종로5가
ValueCountFrequency (%)
동대문역사문화공원 3
 
1.1%
노원 2
 
0.7%
뚝섬 2
 
0.7%
강남 2
 
0.7%
삼각지 2
 
0.7%
신대방 2
 
0.7%
합정 2
 
0.7%
가락시장 2
 
0.7%
잠실 2
 
0.7%
신당 2
 
0.7%
Other values (248) 259
92.5%
2024-04-30T01:50:14.291209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
32
 
3.7%
26
 
3.0%
) 25
 
2.9%
( 25
 
2.9%
23
 
2.6%
22
 
2.5%
19
 
2.2%
15
 
1.7%
15
 
1.7%
14
 
1.6%
Other values (207) 659
75.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 783
89.5%
Decimal Number 34
 
3.9%
Close Punctuation 25
 
2.9%
Open Punctuation 25
 
2.9%
Control 6
 
0.7%
Space Separator 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
32
 
4.1%
26
 
3.3%
23
 
2.9%
22
 
2.8%
19
 
2.4%
15
 
1.9%
15
 
1.9%
14
 
1.8%
14
 
1.8%
14
 
1.8%
Other values (195) 589
75.2%
Decimal Number
ValueCountFrequency (%)
3 9
26.5%
1 5
14.7%
2 5
14.7%
5 5
14.7%
4 4
11.8%
6 3
 
8.8%
7 2
 
5.9%
8 1
 
2.9%
Close Punctuation
ValueCountFrequency (%)
) 25
100.0%
Open Punctuation
ValueCountFrequency (%)
( 25
100.0%
Control
ValueCountFrequency (%)
6
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 783
89.5%
Common 92
 
10.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
32
 
4.1%
26
 
3.3%
23
 
2.9%
22
 
2.8%
19
 
2.4%
15
 
1.9%
15
 
1.9%
14
 
1.8%
14
 
1.8%
14
 
1.8%
Other values (195) 589
75.2%
Common
ValueCountFrequency (%)
) 25
27.2%
( 25
27.2%
3 9
 
9.8%
6
 
6.5%
1 5
 
5.4%
2 5
 
5.4%
5 5
 
5.4%
4 4
 
4.3%
6 3
 
3.3%
2
 
2.2%
Other values (2) 3
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 783
89.5%
ASCII 92
 
10.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
32
 
4.1%
26
 
3.3%
23
 
2.9%
22
 
2.8%
19
 
2.4%
15
 
1.9%
15
 
1.9%
14
 
1.8%
14
 
1.8%
14
 
1.8%
Other values (195) 589
75.2%
ASCII
ValueCountFrequency (%)
) 25
27.2%
( 25
27.2%
3 9
 
9.8%
6
 
6.5%
1 5
 
5.4%
2 5
 
5.4%
5 5
 
5.4%
4 4
 
4.3%
6 3
 
3.3%
2
 
2.2%
Other values (2) 3
 
3.3%

턴스타일개집표기
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct39
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.7728938
Minimum0
Maximum51
Zeros168
Zeros (%)61.5%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-04-30T01:50:14.463083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q319
95-th percentile34.4
Maximum51
Range51
Interquartile range (IQR)19

Descriptive statistics

Standard deviation12.802217
Coefficient of variation (CV)1.4592924
Kurtosis0.47106842
Mean8.7728938
Median Absolute Deviation (MAD)0
Skewness1.2374404
Sum2395
Variance163.89676
MonotonicityNot monotonic
2024-04-30T01:50:14.609227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 168
61.5%
19 8
 
2.9%
34 5
 
1.8%
23 5
 
1.8%
20 5
 
1.8%
21 4
 
1.5%
18 4
 
1.5%
13 4
 
1.5%
16 4
 
1.5%
26 4
 
1.5%
Other values (29) 62
 
22.7%
ValueCountFrequency (%)
0 168
61.5%
3 1
 
0.4%
5 2
 
0.7%
6 1
 
0.4%
7 2
 
0.7%
9 1
 
0.4%
10 4
 
1.5%
11 2
 
0.7%
12 3
 
1.1%
13 4
 
1.5%
ValueCountFrequency (%)
51 1
 
0.4%
49 1
 
0.4%
48 2
 
0.7%
44 1
 
0.4%
40 2
 
0.7%
38 1
 
0.4%
37 3
1.1%
36 1
 
0.4%
35 2
 
0.7%
34 5
1.8%

슬림개집표기
Real number (ℝ)

ZEROS 

Distinct23
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2893773
Minimum0
Maximum72
Zeros241
Zeros (%)88.3%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-04-30T01:50:14.720211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile16
Maximum72
Range72
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.4453562
Coefficient of variation (CV)3.6889316
Kurtosis30.862114
Mean2.2893773
Median Absolute Deviation (MAD)0
Skewness5.1010115
Sum625
Variance71.324041
MonotonicityNot monotonic
2024-04-30T01:50:14.876759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 241
88.3%
7 4
 
1.5%
16 3
 
1.1%
4 3
 
1.1%
11 3
 
1.1%
28 2
 
0.7%
14 1
 
0.4%
8 1
 
0.4%
32 1
 
0.4%
40 1
 
0.4%
Other values (13) 13
 
4.8%
ValueCountFrequency (%)
0 241
88.3%
3 1
 
0.4%
4 3
 
1.1%
5 1
 
0.4%
7 4
 
1.5%
8 1
 
0.4%
9 1
 
0.4%
10 1
 
0.4%
11 3
 
1.1%
14 1
 
0.4%
ValueCountFrequency (%)
72 1
0.4%
64 1
0.4%
40 1
0.4%
38 1
0.4%
34 1
0.4%
32 1
0.4%
30 1
0.4%
28 2
0.7%
25 1
0.4%
24 1
0.4%

플랩형개집표기
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.3113553
Minimum0
Maximum52
Zeros122
Zeros (%)44.7%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-04-30T01:50:15.090882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8
Q314
95-th percentile25.4
Maximum52
Range52
Interquartile range (IQR)14

Descriptive statistics

Standard deviation9.4815096
Coefficient of variation (CV)1.1407898
Kurtosis2.3851137
Mean8.3113553
Median Absolute Deviation (MAD)8
Skewness1.3050468
Sum2269
Variance89.899025
MonotonicityNot monotonic
2024-04-30T01:50:15.274290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 122
44.7%
8 15
 
5.5%
16 13
 
4.8%
12 12
 
4.4%
10 12
 
4.4%
14 11
 
4.0%
9 9
 
3.3%
11 9
 
3.3%
18 8
 
2.9%
19 8
 
2.9%
Other values (21) 54
19.8%
ValueCountFrequency (%)
0 122
44.7%
6 5
 
1.8%
7 7
 
2.6%
8 15
 
5.5%
9 9
 
3.3%
10 12
 
4.4%
11 9
 
3.3%
12 12
 
4.4%
13 7
 
2.6%
14 11
 
4.0%
ValueCountFrequency (%)
52 1
0.4%
49 1
0.4%
41 1
0.4%
35 1
0.4%
34 1
0.4%
32 1
0.4%
31 2
0.7%
30 2
0.7%
29 1
0.4%
28 2
0.7%

표준형개집표기
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58241758
Minimum0
Maximum32
Zeros261
Zeros (%)95.6%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-04-30T01:50:15.406226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum32
Range32
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.4844168
Coefficient of variation (CV)5.9826779
Kurtosis51.229461
Mean0.58241758
Median Absolute Deviation (MAD)0
Skewness7.0065296
Sum159
Variance12.14116
MonotonicityNot monotonic
2024-04-30T01:50:15.499927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 261
95.6%
5 2
 
0.7%
2 2
 
0.7%
14 1
 
0.4%
32 1
 
0.4%
13 1
 
0.4%
27 1
 
0.4%
23 1
 
0.4%
25 1
 
0.4%
4 1
 
0.4%
ValueCountFrequency (%)
0 261
95.6%
2 2
 
0.7%
4 1
 
0.4%
5 2
 
0.7%
7 1
 
0.4%
13 1
 
0.4%
14 1
 
0.4%
23 1
 
0.4%
25 1
 
0.4%
27 1
 
0.4%
ValueCountFrequency (%)
32 1
0.4%
27 1
0.4%
25 1
0.4%
23 1
0.4%
14 1
0.4%
13 1
0.4%
7 1
0.4%
5 2
0.7%
4 1
0.4%
2 2
0.7%

개방형개집표기
Categorical

IMBALANCE 

Distinct3
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
0
271 
5
 
1
19
 
1

Length

Max length2
Median length1
Mean length1.003663
Min length1

Unique

Unique2 ?
Unique (%)0.7%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 271
99.3%
5 1
 
0.4%
19 1
 
0.4%

Length

2024-04-30T01:50:15.605814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T01:50:15.685164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 271
99.3%
5 1
 
0.4%
19 1
 
0.4%

스피드개집표기
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1831502
Minimum0
Maximum6
Zeros157
Zeros (%)57.5%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-04-30T01:50:15.756417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5797925
Coefficient of variation (CV)1.3352426
Kurtosis-0.026973571
Mean1.1831502
Median Absolute Deviation (MAD)0
Skewness1.0420438
Sum323
Variance2.4957445
MonotonicityNot monotonic
2024-04-30T01:50:15.855685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 157
57.5%
2 46
 
16.8%
4 29
 
10.6%
3 23
 
8.4%
1 12
 
4.4%
6 4
 
1.5%
5 2
 
0.7%
ValueCountFrequency (%)
0 157
57.5%
1 12
 
4.4%
2 46
 
16.8%
3 23
 
8.4%
4 29
 
10.6%
5 2
 
0.7%
6 4
 
1.5%
ValueCountFrequency (%)
6 4
 
1.5%
5 2
 
0.7%
4 29
 
10.6%
3 23
 
8.4%
2 46
 
16.8%
1 12
 
4.4%
0 157
57.5%
Distinct5
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
0
148 
2
66 
4
47 
6
 
10
8
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 148
54.2%
2 66
24.2%
4 47
 
17.2%
6 10
 
3.7%
8 2
 
0.7%

Length

2024-04-30T01:50:15.969639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T01:50:16.055850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 148
54.2%
2 66
24.2%
4 47
 
17.2%
6 10
 
3.7%
8 2
 
0.7%

엘레베이터용개집표기
Categorical

IMBALANCE 

Distinct3
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
0
240 
1
 
18
2
 
15

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 240
87.9%
1 18
 
6.6%
2 15
 
5.5%

Length

2024-04-30T01:50:16.148561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T01:50:16.238985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 240
87.9%
1 18
 
6.6%
2 15
 
5.5%

Interactions

2024-04-30T01:50:11.915875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:07.252346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:07.924969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:08.557635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:09.176976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:09.779446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:10.433043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:11.255194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:12.002228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:07.322087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:08.004305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:08.632797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:09.242529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:09.868593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:10.516059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:11.327282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:12.083423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:07.403201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:08.096799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:08.710664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:09.318215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:09.953343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:10.594875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:11.420572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:12.160196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:07.476637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:08.167624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:08.778319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:09.383420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:10.024272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:10.661564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:11.510252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:12.244270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:07.552991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:08.245059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:08.852768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:09.468779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:10.093483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:10.742833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:11.596190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:12.338203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:07.651366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:08.331121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:08.927685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:09.556445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:10.181179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:10.831093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:11.679171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:12.423796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:07.727075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:08.402644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:09.014819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:09.625392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:10.270818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:10.899500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:11.748946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:12.523257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:07.827810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:08.482174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:09.102771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:09.703072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:10.352102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:11.188330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:11.830325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T01:50:16.312944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번호선역번호턴스타일개집표기슬림개집표기플랩형개집표기표준형개집표기개방형개집표기스피드개집표기우대자용개집표기엘레베이터용개집표기
연번1.0000.9220.9160.7140.2920.5960.1290.0000.6280.7230.269
호선0.9221.0000.9940.6450.4640.6240.1850.0000.6390.5910.344
역번호0.9160.9941.0000.7080.3490.7260.0000.0120.6900.4720.231
턴스타일개집표기0.7140.6450.7081.0000.0000.4900.0000.0000.7730.6030.152
슬림개집표기0.2920.4640.3490.0001.0000.0000.4180.0000.4140.0000.000
플랩형개집표기0.5960.6240.7260.4900.0001.0000.0000.0000.5820.6490.447
표준형개집표기0.1290.1850.0000.0000.4180.0001.0000.0000.0950.5920.000
개방형개집표기0.0000.0000.0120.0000.0000.0000.0001.0000.0000.0000.000
스피드개집표기0.6280.6390.6900.7730.4140.5820.0950.0001.0000.4780.248
우대자용개집표기0.7230.5910.4720.6030.0000.6490.5920.0000.4781.0000.176
엘레베이터용개집표기0.2690.3440.2310.1520.0000.4470.0000.0000.2480.1761.000
2024-04-30T01:50:16.695118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우대자용개집표기엘레베이터용개집표기개방형개집표기
우대자용개집표기1.0000.1330.000
엘레베이터용개집표기0.1331.0000.000
개방형개집표기0.0000.0001.000
2024-04-30T01:50:16.779156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번호선역번호턴스타일개집표기슬림개집표기플랩형개집표기표준형개집표기스피드개집표기개방형개집표기우대자용개집표기엘레베이터용개집표기
연번1.0000.9890.996-0.767-0.3940.786-0.085-0.8340.0000.3800.164
호선0.9891.0000.983-0.769-0.3960.792-0.076-0.8350.0000.4130.231
역번호0.9960.9831.000-0.773-0.4020.784-0.088-0.8380.0120.3970.221
턴스타일개집표기-0.767-0.769-0.7731.0000.162-0.764-0.0700.9100.0000.2900.089
슬림개집표기-0.394-0.396-0.4020.1621.000-0.3650.1520.4380.0000.0000.000
플랩형개집표기0.7860.7920.784-0.764-0.3651.000-0.108-0.8250.0000.4430.218
표준형개집표기-0.085-0.076-0.088-0.0700.152-0.1081.000-0.0310.0000.4310.000
스피드개집표기-0.834-0.835-0.8380.9100.438-0.825-0.0311.0000.0000.3300.170
개방형개집표기0.0000.0000.0120.0000.0000.0000.0000.0001.0000.0000.000
우대자용개집표기0.3800.4130.3970.2900.0000.4430.4310.3300.0001.0000.133
엘레베이터용개집표기0.1640.2310.2210.0890.0000.2180.0000.1700.0000.1331.000

Missing values

2024-04-30T01:50:12.636645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T01:50:12.795820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

연번호선역번호역사명턴스타일개집표기슬림개집표기플랩형개집표기표준형개집표기개방형개집표기스피드개집표기우대자용개집표기엘레베이터용개집표기
011150서울역(1)29160140440
121151시청(1)064000400
231152종각3711000500
341153종로3가(1)340000400
451154종로5가310000300
561155동대문(1)250000300
671156신설동(1)2311000400
781157제기동200000200
891158청량리360000300
9101159동묘앞120000200
연번호선역번호역사명턴스타일개집표기슬림개집표기플랩형개집표기표준형개집표기개방형개집표기스피드개집표기우대자용개집표기엘레베이터용개집표기
26326482819문정001700020
26426582820장지001700020
26526682821복정00800020
26626782822산성001100020
26726882823남한산성입구001300041
26826982824단대오거리001300022
26927082825신흥00600020
27027182826수진00900020
27127282827모란00600041
27227382828남위례00070040